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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Comparative modeling reveals the molecular determinants of aneuploidy fitness cost in a wild yeast model

Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will be maintained only if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74%–94% of the variance in aneuploid strains’ growth rates is explained by the cumulative cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of small nucleolar RNAs (snoRNAs) and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution.

genic load↗
DOE OSTI2021

Preclinical tumor organoid models in personalized cancer therapy: Not everyone fits the mold

Highlights: • In personalized cancer medicine, each patient receives a specific treatment. • The tumor microenvironment and interpersonal differences can affect treatment. • Organoid culture systems fully recapitulate the tumor microenvironments. In contrast to conventional cancer treatment, in personalized cancer medicine each patient receives a specific treatment. The response to therapy, clinical outcomes, and tumor behavior such as metastases, tumor progression, carcinogenesis can be significantly affected by the heterogeneous tumor microenvironment (TME) and interpersonal differences. Therefore, using native tumor microenvironment mimicking models is necessary to improving personalized cancer therapy. Both in vitro 2D cell culture and in vivo animal models poorly recapitulate the heterogeneous tumor (immune) microenvironments of native tumors. The development of 3D culture models, native tumor microenvironment mimicking models, made it possible to evaluate the chemoresistance of tumor tissue and the functionality of drugs in the presence of cell-extracellular matrix and cell-cell interactions in a 3D construction. Various personalized tumor models have been designed to preserving the native tumor microenvironment, including patient-derived tumor xenografts and organoid culture strategies. In this review, we will discuss the patient-derived organoids as a native tumor microenvironment mimicking model in personalized cancer therapy. In addition, we will also review the potential and the limitations of organoid culture systems for predicting patient outcomes and preclinical drug screening. Finally, we will discuss immunotherapy drug screening in tumor organoids by using microfluidic technology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Alanine 501 Mutations in Penicillin-Binding Protein 2 from Neisseria gonorrhoeae : Structure, Mechanism, and Effects on Cephalosporin Resistance and Biological Fitness

Resistance of Neisseria gonorrhoeae to expanded-spectrum cephalosporins such as ceftriaxone and cefixime has increased markedly in the past decade. The primary cephalosporin resistance determinant is a mutated penA gene, which encodes the essential peptidoglycan transpeptidase, penicillin-binding protein 2 (PBP2). Decreased susceptibility and resistance can be conferred by mosaic penA alleles containing upward of 60 amino acid changes relative to wild-type PBP2, or by nonmosaic alleles with relatively few mutations, the most important of which occurs at Ala501 located near the active site of PBP2. Recently, fully cefixime- and ceftriaxone-resistant clinical isolates that harbored a mosaic penA allele with an A501P mutation were identified. To examine the potential of mutations at Ala501 to increase resistance to expanded-spectrum cephalosporins, we randomized codon 501 in a mosaic penA allele and transformed N. gonorrhoeae to increased cefixime resistance. Interestingly, only five substitutions of Ala501 (A501V, A501T, A501P, A501R, and A501S) that increased resistance and preserved essential transpeptidase function were isolated. To understand their structural implications, these mutations were introduced into the nonmosaic PBP2-6140CT, which contains four C-terminal mutations present in PBP2 from the penicillin-resistant strain FA6140. The crystal structure of PBP2-6140CT-A501T was determined and revealed ordering of a loop near the active site and a new hydrogen bond involving Thr501 that connects the loop and the SxxK conserved active site motif. The structure suggests that increased rigidity in the active site region is a mechanism for cephalosporin resistance mediated by Ala501 mutations in PBP2.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

tinyIFD: A High-Throughput Binding Pose Refinement Workflow Through Induced-Fit Ligand Docking

A critical step in structure-based drug discovery is predicting whether and how a candidate molecule binds to a model of a therapeutic target. However, substantial protein side chain movements prevent current screening methods, such as docking, from accurately predicting the ligand conformations and require expensive refinements to produce viable candidates. Here, we present the development of a high-throughput and flexible ligand pose refinement workflow, called “tinyIFD”. The main features of the workflow include the use of specialized high-throughput, small-system MD simulation code mdgx.cuda and an actively learning model zoo approach. We show the application of this workflow on a large test set of diverse protein targets, achieving 66% and 76% success rates for finding a crystal-like pose within the top-2 and top-5 poses, respectively. We also applied this workflow to the SARS-CoV-2 main protease (M pro ) inhibitors, where we demonstrate the benefit of the active learning aspect in this workflow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗